MMEA (The Measurement, Monitoring and Environmental Efficiency Assessment) research program final seminar presentation by Senior Scientist Jarmo Koistinen, Finnish Meteorological Institute

1. Probabilistic weather forecasts for risk
management of extreme events
Jarmo Koistinen1, Juha Kilpinen1, and Mari Heinonen2
1 Finnish Meterological Institute
2 Helsinki Regions Environmental Services Authority HSY, Water
management, Wastewater treatment

2. Weather and mining
1. Environmental measurements (e.g. weather radars)
including open data, Big data, crowdsourcing and IOT
2. Diagnosis and probabilistic prediction of high-impact
weather: nowcasting, numerical weather prediction,
seasonal and climate forecasts
3. Diagnosis and prediction of weather-induced conditions
(flooding, storm water hydrology and hydraulics, water
and air pollution, working and process conditions):
• Environmental impacts on mining processes
• Mining process impacts on the environment
4. Support data for the adaptation of weather and water
impacts in the mining processes:
• Risk management (mitigation actions)
• Optimization (situational awareness and automatic
tuning of processes)
Chainedserviceprocess
Content,qualityandICTspecifications

3. Objective: Proactive optimization and risk management
of mining processes that depend on high-impact weather,
in most cases extreme rainfall

4. Economic risk of a future weather event =
{probability of the event} x
{expected losses induced by the event}
Example: 0.01 (1 %) x 1000 M€ = 1 (100 %) x 10 M€

5. Rainfall event = exceedance of a fixed
accumulation during a period, e.g.
250 mm/week
Forecast of accumulated rainfall
Expectedlosses
1k€
1M€
1000 M€
Uncertainty
Uncertainty can
be characterized
with the aid of
probabilities

6. Conclusion: Forecasts of probabilities are
vitally important for decision making –
and reasonable in meteorological sense
An example at a specific location and time period:
Probability of exceeding 50 mm during the next week = 98 %
Probability of exceeding 100 mm during the next week = 30 %
Probability of exceeding 200 mm during the next week = 1 %
The tool for obtaining exceedance probabilities is
ensemble prediction system (EPS) i.e. instead of a single
forecast we compute multiple alternative scenarios
that estimate probabilities of high-impact events.
Statistical matching of the predicted probabilities
with observational data will improve event
predictions – but it is not trivial!

7. Weather radar based movement of precipitating
areas is the basis for 0 - 3 (-6) h long nowcasts
• Pilot projects (Tekes/RAVAKE &
MMEA; EU/HAREN & EDHIT):
Probabilities computed from 51
members of ensemble
forecasts (Koistinen et al. 2012)
• Measurement accuracy and
quality (MMEA/WP 3/Task 4)
• Computationally demanding
• Growth and decay of rainfall
systems not covered =>
extrapolative prediction skill
becomes low in 1-6 hours
• Update cycle 5-15 min

8. Numerical weather prediction NWP is required
for lead times 3h - 15 days (- seasons)
• Data assimilation
- chaotic equations forecast initial state important
- problem : observations inaccurate, spatially/temporally sparse
- remedy : model gives a more complete state of the atmosphere
- solution : combine observations with an earlier forecast (”first guess field") to
form the initial state of the forecast = Data assimilation
• Method used in Hirlam : 4DVAR = four-dimensional variational data assimilation
• State-of-the-art : 4DVAR used only in very few LAM models worldwide
• Considerable resources devoted to pre-processing, quality control, tuning and
assimilation of the data!
Forecast model
Forecasts
Analyses
First guess field
Data assimilation system
Observations
Forecast
initial state

9. Physical laws are presented
in a form that a computer can
compute the future state of
atmosphere from the present
state of atmosphere.
All physical variables
(temperature, pressure,
humidity, …) are presented in
a grid with several layers.
The typical distance between
grid points is 3-15 km. The
number of vertical levels
varies typically between 50
and 150.
Limitations:
• Update cycle (3-) 6 -12 h
• NWP not good in
predicting the proper time
and place of convective
rain storms
Global forecast model

10. Deterministic Forecasting
Forecast time
Temperature
Initial condition Forecast
Is this forecast “correct”?
Initial
uncertainty
Model error and chaotic atmosphere

11. Ensemble Forecasting
Forecast time
Temperature
Alternative scenarios of the predicted future in terms of the Ensemble ≈
the real Probability Distribution (PDF) => Exceedance probabilities
Initial condition Forecast
Perturbed initial conditions
Stochastic physics

12. Global EPS system at ECMWF
• 1 control run + 50 perturbed runs
• An ensemble forecast provides
probablities of (extreme) events
e.g. probability of precipitation
over 50 mm in next 10 days for a
certain area or location.
• Forecasts are available 10-15
days ahead

13. Present time
Gauge or radar
measurements
of rainfall, river
flow measurements
Previous week-months
Rain and river flow forecasts,
Next week
105 mm
probability > 10 mm = 95 %
probability > 20 mm = 40 %
probability > 30 mm = 10 %
Most likely accumulation = 17 mm
An example of forecast content for a mining location
or for a river catchment interacting with the mine
An actual pilot exists at the Kittilä gold mine: Rainfall forecasts (FMI) ->
Hydrological model of Seurujoki (SYKE) -> products (Agnico Eagle)
Note: Automatic real-time weather and water
impact models of the user’s processes and risks
are still weakly developed in Finland.

14. Operational application at HSY
Objectives
• Alarming of predicted influent increase
(capacity problems possible in extreme
cases)
• Bypass flow minimization (environment
risk)
• Adaptive process actions, e.g. optimize
influent tunnel volume (pumping)
Precipitation
nowcast ensemble
(5, 50 and 90 %)
Rainfall-Runoff model
1 mm ~ 25 000 m³
Supply tunnel
Wastewater influent flow
Storm water inflow
forecast
Viikinmäki WWTP
Water level
Treatment capacity
and process condition
Flow adjustment
Decision support
centre
Pumping
Total influent flow
200 000 – 800 000 m³/day

15. • “Smart mining processes” are
presently rather primitive in
responding adaptively on future
weather and water risks and
impacts
• Probabilistic predictions of high-
impact weather, especially
rainfall, can be valuable for
proactive risk management and
optimization of mining processes
• Chaining of probabilistic weather
predictions with impact models
(e.g. hydrology, hydraulics,
mining processes) can offer
valuable automatic tools for
decision support
Conclusions